Tags
Language
Tags
June 2025
Su Mo Tu We Th Fr Sa
1 2 3 4 5 6 7
8 9 10 11 12 13 14
15 16 17 18 19 20 21
22 23 24 25 26 27 28
29 30 1 2 3 4 5
    Attention❗ To save your time, in order to download anything on this site, you must be registered 👉 HERE. If you do not have a registration yet, it is better to do it right away. ✌

    https://sophisticatedspectra.com/article/drosia-serenity-a-modern-oasis-in-the-heart-of-larnaca.2521391.html

    DROSIA SERENITY
    A Premium Residential Project in the Heart of Drosia, Larnaca

    ONLY TWO FLATS REMAIN!

    Modern and impressive architectural design with high-quality finishes Spacious 2-bedroom apartments with two verandas and smart layouts Penthouse units with private rooftop gardens of up to 63 m² Private covered parking for each apartment Exceptionally quiet location just 5–8 minutes from the marina, Finikoudes Beach, Metropolis Mall, and city center Quick access to all major routes and the highway Boutique-style building with only 8 apartments High-spec technical features including A/C provisions, solar water heater, and photovoltaic system setup.
    Whether for living or investment, this is a rare opportunity in a strategic and desirable location.

    Privacy-Preserving Machine Learning (Final Release)

    Posted By: yoyoloit
    Privacy-Preserving Machine Learning (Final Release)

    Privacy-Preserving Machine Learning
    by J. Morris Chang, Di Zhuang, and G. Dumindu Samaraweera

    English | 2023 | ISBN: 1617298042 | 335 pages | True PDF | 26.41 MB


    Keep sensitive user data safe and secure without sacrificing the performance and accuracy of your machine learning models.

    In Privacy Preserving Machine Learning, you will learn:

    Privacy considerations in machine learning
    Differential privacy techniques for machine learning
    Privacy-preserving synthetic data generation
    Privacy-enhancing technologies for data mining and database applications
    Compressive privacy for machine learning


    Privacy-Preserving Machine Learning is a comprehensive guide to avoiding data breaches in your machine learning projects. You’ll get to grips with modern privacy-enhancing techniques such as differential privacy, compressive privacy, and synthetic data generation. Based on years of DARPA-funded cybersecurity research, ML engineers of all skill levels will benefit from incorporating these privacy-preserving practices into their model development. By the time you’re done reading, you’ll be able to create machine learning systems that preserve user privacy without sacrificing data quality and model performance.

    Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

    About the Technology

    Machine learning applications need massive amounts of data. It’s up to you to keep the sensitive information in those data sets private and secure. Privacy preservation happens at every point in the ML process, from data collection and ingestion to model development and deployment. This practical book teaches you the skills you’ll need to secure your data pipelines end to end.

    About the Book

    Privacy-Preserving Machine Learning explores privacy preservation techniques through real-world use cases in facial recognition, cloud data storage, and more. You’ll learn about practical implementations you can deploy now, future privacy challenges, and how to adapt existing technologies to your needs. Your new skills build towards a complete security data platform project you’ll develop in the final chapter.

    What’s Inside

    Differential and compressive privacy techniques
    Privacy for frequency or mean estimation, naive Bayes classifier, and deep learning
    Privacy-preserving synthetic data generation
    Enhanced privacy for data mining and database applications